Abstract
Frozen visual foundation models provide transferable features for visual place recognition, but fixed aggregation can suppress useful distinctions in new environments. We introduce TFA, a reliability-guided, training-free aggregation method requiring neither place labels nor task-specific weight updates. Our key observation is that reproducible retrieval need not be discriminative: independent codebooks can consistently retrieve a few database hubs. TFA combines cross-codebook agreement, retrieval coverage, and spectral statistics to control residual assignment, spectral shaping, and global-feature fusion. Its spectral kernel exactly recovers original descriptor similarity at zero intervention. Database-only TFA fixes its rules before accessing queries; TFA-C64 uses 64 disjoint unlabeled target images to calibrate retrieval for subsequent queries. Across 20 ground protocols with a fixed DINOv2-B backbone and matched resolution, database-only TFA improves Recall@1 over AnyLoc by 17.39 percentage points on MSLS-val and 9.55 on SPED. C64 mitigates failures of database-only calibration in driving environments. Across eight aerial/cross-view protocols, TFA achieves the highest Recall@1 among compared training-free heads in 14 of 16 DINOv2/DINOv3 backbone-protocol combinations. In a separate native-system comparison, DINOv2-G-based TFA-C64 reaches 91.46% Recall@1 on Pitts30k and 76.29% on VPAIR, outperforming the displayed training-free comparators on all five benchmarks. These results show that reliability-guided aggregation can recover additional retrieval capability from frozen representations, providing a practical baseline for new environments with scarce place supervision.
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Publication details
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- Green open access
Cite this article
APA 7
Li, X., Mao, Z., Wang, S., Duan, S., & Zhang, G. (2026). Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition. https://omanscience.com/en/articles/calibrating-retrieval-geometry-reliability-guided-training-free-aggregation-for-visual-place-recognition
MLA 9
Li, Xin, et al. "Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition." https://omanscience.com/en/articles/calibrating-retrieval-geometry-reliability-guided-training-free-aggregation-for-visual-place-recognition.
Chicago (author–date)
Li, Xin, Zhimin Mao, Shang Wang, Siyuan Duan, and Geng Zhang. 2026. "Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition." https://omanscience.com/en/articles/calibrating-retrieval-geometry-reliability-guided-training-free-aggregation-for-visual-place-recognition.
Harvard
Li, X., Mao, Z., Wang, S., Duan, S. and Zhang, G. (2026) 'Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition', Available at: https://omanscience.com/en/articles/calibrating-retrieval-geometry-reliability-guided-training-free-aggregation-for-visual-place-recognition.
Vancouver
Li X, Mao Z, Wang S, Duan S, Zhang G. Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition. https://omanscience.com/en/articles/calibrating-retrieval-geometry-reliability-guided-training-free-aggregation-for-visual-place-recognition
IEEE
X. Li, Z. Mao, S. Wang, S. Duan, and G. Zhang, "Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition," https://omanscience.com/en/articles/calibrating-retrieval-geometry-reliability-guided-training-free-aggregation-for-visual-place-recognition.